{"__content__"=>"\n Objectives:\n \n \n Methods:\n \n \n Results:\n \n \n Conclusions:\n \n ", "p"=>[{"__content__"=>"We analyze street-segment-level property crime trends in Barcelona from 2010 to 2018 using group-based trajectory modeling (GBTM), with two main goals: to assess the applicability of the law of crime concentration in a European context, and to investigate the temporal stability of hot, cool, and cold spots."}, {"__content__"=>"GBTM is implemented using a Zero-Inflated Poisson (ZIP) model to account for over-dispersion, identifying latent trajectory groups. A post-hoc validation is conducted through: (a) comparison with time-series clustering to assess alignment, and (b) an analysis of posterior probability distributions to evaluate classification robustness."}, {"__content__"=>"Nine distinct crime trajectory groups are identified. Three of these groups, which together include just 1% of street segments, account for nearly 30% of total property crimes. While fluctuations occur among groups, the dominance of a small fraction of high-crime street segments remains, confirming the concentration of crime in specific areas. Notably, some hot and cool spots exhibit increasing trends, pointing to the emergence of new crime areas. A strong nested relationship between GBTM groups and time-series clusters further reinforces the validity of the classification. Posterior probability distributions also show clear separation among groups."}, {"__content__"=>"This study supports the law of crime concentration in Barcelona while revealing specific local dynamics that differ from those found in North American cities. It demonstrates the effectiveness of combining GBTM with post-hoc analysis to examine micro-level crime patterns, offering a transferable methodological framework for other urban contexts. These findings have important implications for crime prevention and urban policy in European settings."}]}